To extend our work to another accessible dataset, we chose to also use a part of the SEWA dataset [1]}.
We used the German set of the cross-cultural Emotion Database SEWA that is composed of 24 audiovisual recordings of dyadic conversations as showed by Table REF , sampled at 16kHz.
Pairs are discussing for less than 3 minutes about an advert seen beforehand.
As they are two speakers per document, Kossaifi et al. [1]} chose to duplicate each document and to add a flag denoting the current speaker, therefore there are 48 documents in the database.
The SEWA corpus is now a reference in the community, as it has been used in the two last Audio/Visual Emotion Challenges (AVEC) [3]}, [4]}.
A continuous annotation among three dimensions (arousal, valence and liking) was made by six annotators and sampled every 100 ms.
The liking axis describes how much the subjects liked the advert.
The corpus is divided into two subsets: the Train set containing 34 documents (i.e. 17 conversations) and and the Development set containing 14 documents (i.e. 7 conversations). Test gold references are not distributed.
Annotations were merged to form a gold reference for both datasets, used in the prediction task.
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